Are Broadly Neutralizing Antibodies against HIV-1 Unusual? (47.32)
Bibliographic record
Abstract
Abstract To date, AIDS vaccines cannot induce broadly(b)-neutralizing (Nt) antibodies (Abs) against HIV-1. It has been suggested that HIV’s Nt epitopes mimic “self” epitopes, and that bNt Abs are rare because they develop from autoreactive B cell clones that are normally deleted or made anergic by self antigens (Agns); such Abs are thought to have long CDR-H3 loops (H3s). Thus, self-tolerance must be broken to accumulate self-reactive naïve B cells whose Abs recognize Nt, HIV-1 epitopes. It has been reported that most of the bNt monoclonal (M)Abs have very long H3s, and that most react with self molecules (e.g., cardiolipin, CL). Alternatively, bNt Abs may arise out of “normal” Ab responses, with HIV Agns selecting bNt Abs with long H3s, and/or long H3s may develop via processes driven by persistent Agn. We have begun to test these models by several approaches. Using autoAgn ELISAs and microarrays, bNt MAbs were shown not to be unusually self-reactive, compared to genuine autoreactive MAbs or autoimmune sera; also, bNt sera did not react significantly with CL. Finally, sequence analysis of ~700 human MAbs indicated that the bNt MAbs are no different than non-Nt MAbs against HIV-1 or other chronic viruses, or autoimmune anti-protein MAbs; but they are very different from autoimmune MAbs against non-protein autoAgns. Taken together, our results support the conclusion that HIV-bNt Abs are not unusual in their reactivity or structures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".